Cancer Imaging最新文献

{"title":"Diagnostic model using LI-RADS v2018 for predicting early recurrence of microvascular invasion-negative solitary hepatocellular carcinoma.","authors":"Yingying Liang, Xiaorui Han, Tingwen Zhou, Chuyin Xiao, Changzheng Shi, Xinhua Wei, Hongzhen Wu","doi":"10.1186/s40644-025-00865-1","DOIUrl":"https://doi.org/10.1186/s40644-025-00865-1","url":null,"abstract":"<p><strong>Objectives: </strong>To develop a diagnostic model for predicting the early recurrence of microvascular invasion (MVI)-negative hepatocellular carcinoma (HCC) after surgical resection, using the Liver Imaging Reporting and Data System (LI-RADS) version 2018.</p><p><strong>Methods: </strong>This retrospective study included 73 patients with MVI-negative HCC who underwent Gadoxetic acid-enhanced MRI (EOB-MRI) scanning before surgical resection. The clinical factors and LI-RADS v2018 MRI features associated with early recurrence were determined using univariable and multivariable analyses. A diagnostic model predicting early recurrence after surgical resection was developed, and its predictive ability was evaluated via a receiver operating characteristic curve. Then, the recurrence-free survival (RFS) rates were analyzed by Kaplan-Meier method.</p><p><strong>Results: </strong>In total, 26 (35.6%) patients were diagnosed with early recurrence according to the follow-up results. Infiltrative appearance and targetoid hepatobiliary phase (HBP) appearance were independent predictors associated with early recurrence (p < 0.05). For the established diagnostic model that incorporated these two significant predictors, the AUC value was 0.76 (95% CI: 0.64-0.85) for predicting early recurrence after resection, which was higher than the infiltrative appearance (AUC: 0.67, 95% CI: 0.55-0.78, p = 0.019) and targetoid HBP appearance (AUC: 0.68, 95% CI:0.57-0.79, p = 0.028). In the RFS analysis, patients with infiltrative appearance and targetoid HBP appearance showed significantly lower RFS rates than those without infiltrative appearance (2-year RFS rate, 48.0% vs. 72.0%; p = 0.009) and targetoid HBP appearance (2-year RFS rate, 60.0% vs. 35.0%; p = 0.003).</p><p><strong>Conclusion: </strong>An EOB-MRI model based on infiltrative appearance and targetoid HBP appearance showed good performance in predicting early recurrence of HCC after surgery, which may provide personalized guidance for clinical treatment decisions in patients with MVI-negative HCC.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"46"},"PeriodicalIF":3.5,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143750951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based breast MRI for predicting axillary lymph node metastasis: a systematic review and meta-analysis.","authors":"Chia-Fen Lee, Joseph Lin, Yu-Len Huang, Shou-Tung Chen, Chen-Te Chou, Dar-Ren Chen, Wen-Pei Wu","doi":"10.1186/s40644-025-00863-3","DOIUrl":"https://doi.org/10.1186/s40644-025-00863-3","url":null,"abstract":"<p><strong>Background: </strong>To perform a systematic review and meta-analysis that assesses the diagnostic performance of deep learning algorithms applied to breast MRI for predicting axillary lymph nodes metastases in patients of breast cancer.</p><p><strong>Methods: </strong>A systematic literature search in PubMed, MEDLINE, and Embase databases for articles published from January 2004 to February 2025. Inclusion criteria were: patients with breast cancer; deep learning using MRI images was applied to predict axillary lymph nodes metastases; sufficient data were present; original research articles. Quality Assessment of Diagnostic Accuracy Studies-AI and Checklist for Artificial Intelligence in Medical Imaging was used to assess the quality. Statistical analysis included pooling of diagnostic accuracy and investigating between-study heterogeneity. A summary receiver operating characteristic curve (SROC) was performed. R statistical software (version 4.4.0) was used for statistical analyses.</p><p><strong>Results: </strong>A total of 10 studies were included. The pooled sensitivity and specificity were 0.76 (95% CI, 0.67-0.83) and 0.81 (95% CI, 0.74-0.87), respectively, with both measures having moderate between-study heterogeneity (I² = 61% and 60%, respectively; p < 0.01). The SROC analysis yielded a weighted AUC of 0.788.</p><p><strong>Conclusion: </strong>This meta-analysis demonstrates that deep learning algorithms applied to breast MRI offer promising diagnostic performance for predicting axillary lymph node metastases in breast cancer patients. Incorporating deep learning into clinical practice may enhance decision-making by providing a non-invasive method to more accurately predict lymph node involvement, potentially reducing unnecessary surgeries.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"44"},"PeriodicalIF":3.5,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143750360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The prognostic significance of semi-quantitative metabolic parameters and tumoral metabolic activity based on ¹²³I-MIBG SPECT/CT in pretreatment neuroblastoma patients.","authors":"Ziang Zhou, Xu Yang, Guanyun Wang, Xiaoya Wang, Jun Liu, Yanfeng Xu, Kan Ying, Wei Wang, Jigang Yang","doi":"10.1186/s40644-025-00858-0","DOIUrl":"https://doi.org/10.1186/s40644-025-00858-0","url":null,"abstract":"<p><strong>Purpose: </strong>To assess the prognosis predictive value of semi-quantitative metabolic parameters and tumoral metabolic activity based on ¹²³I-meta-iodobenzylguanidine (MIBG) SPECT/CT in pretreatment neuroblastoma (NB) patients.</p><p><strong>Methods: </strong>A total of 50 children (25 girls, 25 boys, median age 37 months, range 1-102 months) with newly diagnosed NB, consecutively examined with pretherapeutic ¹²³I-MIBG SPECT/CT between 2018 and 2024, were included in this retrospective study. The semi-quantitative metabolic parameters and activity of primary tumor were measured, including Tmax/Lmax, Tmean/Lmean, Tmax/Lmean, Tmax/Mmax, Tmean/Mmean and asphericity (ASP). The ratio was maximum or mean count of primary tumor, liver and muscle. Clinical data and image-related factors was recorded as well. The outcome endpoint was event-free survival (EFS). Independent predictors were identified through univariate and multivariate logistic regression analyses. Receiver operating characteristic (ROC) and Kaplan Meier analysis with log-rank test for EFS were performed.</p><p><strong>Results: </strong>Median follow-up was 42 months (range 2.5-74 months; 4 patients showed disease progression/relapse, 7 patients died). The univariate and multivariate Cox regression analysis demonstrated that bone/bone marrow metastasis [95% confidence interval (CI): 1.051, 18.570, p = 0.043], Tmax/Lmax (95% CI: 1.074, 1.459, p = 0.004) and ASP (95% CI: 2.618, 273.477, p = 0.006) were independent predictors of EFS. The Kaplan Meier survival analyses demonstrated that Tmax/Lmax undefined[Formula: see text]]]>6 and ASP [Formula: see text]undefined]]>34% and with bone/bone marrow metastasis had worse outcomes.</p><p><strong>Conclusion: </strong>In this exploratory study, pretherapeutic ¹²³I-MIBG image-derived semi-quantitative metabolic parameters and tumor asphericity provided prognostic value for EFS in NB patients. Tmax/Lmax [Formula: see text]undefined]]>6 and ASP [Formula: see text]undefined]]>34%, along with the presence of bone/bone marrow metastasis, could be considered as supplementary factors alongside existing ones.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"45"},"PeriodicalIF":3.5,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143751216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MRI-based habitat imaging predicts high-risk molecular subtypes and early risk assessment of lower-grade gliomas.","authors":"Xiangli Yang, Wenju Niu, Kai Wu, Guoqiang Yang, Hui Zhang","doi":"10.1186/s40644-025-00838-4","DOIUrl":"https://doi.org/10.1186/s40644-025-00838-4","url":null,"abstract":"<p><strong>Background: </strong>In lower-grade gliomas (LrGGs, histological grades 2-3), there exist a minority of high-risk molecular subtypes with malignant transformation potential, associated with unfavorable clinical outcomes and shorter survival prognosis. Identifying high-risk molecular subtypes early in LrGGs and conducting preoperative prognostic evaluations are crucial for precise clinical diagnosis and treatment.</p><p><strong>Materials and methods: </strong>We retrospectively collected data from 345 patients with LrGGs and comprehensively screened key high-risk molecular markers. Based on preoperative MRI sequences (CE-T1WI/T2-FLAIR), we employed seven classifiers to construct models based on habitat, radiomics, and combined. Eventually, we identified Extra Trees based on habitat features as the optimal predictive model for identifying high-risk molecular subtypes of LrGGs. Moreover, we developed a prognostic prediction model based on radiomics score (Radscore) to assess the survival outlook of patients with LrGGs. We utilized Kaplan-Meier (KM) survival analysis alongside the log-rank test to discern variations in survival probabilities among high-risk and low-risk cohorts. The concordance index was employed to gauge the efficacy of habitat, clinical, and amalgamated prognosis models. Calibration curves were utilized to appraise the congruence between the anticipated survival probability and the actual survival probability projected by the models.</p><p><strong>Results: </strong>The habitat model for predicting high-risk molecular subtypes of LrGGs, achieved AUCs of 0.802, 0.771, and 0.768 in the training set, internal test set, and external test set, respectively. Comparison among habitat, clinical, combined prognostic models revealed that the combined prognostic model exhibited the highest performance (C-index = 0.781 in the training set, C-index = 0.778 in the internal test set, C-index = 0.743 in the external test set), followed by the habitat prognostic model (C-index = 0.749 in the training set, C-index = 0.716 in the internal test set, C-index = 0.707 in the external test set), while the clinical prognostic model performed the worst (C-index = 0.717 in the training set, C-index = 0.687 in the internal test set, C-index = 0.649 in the external test set). Furthermore, the calibration curves of the combined model exhibited satisfactory alignment when forecasting the 1-year, 2-year, and 3-year survival probabilities of patients with LrGGs.</p><p><strong>Conclusion: </strong>The MRI-based habitat model simultaneously achieves the objectives of non-invasive prediction of high-risk molecular subtypes of LrGGs and assessment of survival prognosis. This has incremental value for early non-invasive warning of malignant transformation in LrGGs and risk-stratified management.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"43"},"PeriodicalIF":3.5,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11951782/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143742532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differentiating high-grade patterns and predominant subtypes for IASLC grading in invasive pulmonary adenocarcinoma using radiomics and clinical-semantic features.","authors":"Sunyi Zheng, Jiaxin Liu, Jiping Xie, Wenjia Zhang, Keyi Bian, Jing Liang, Jingxiong Li, Jing Wang, Zhaoxiang Ye, Dongsheng Yue, Xiaonan Cui","doi":"10.1186/s40644-025-00864-2","DOIUrl":"https://doi.org/10.1186/s40644-025-00864-2","url":null,"abstract":"<p><strong>Objectives: </strong>The International Association for the Study of Lung Cancer (IASLC) grading system for invasive non-mucinous adenocarcinoma (ADC) incorporates high-grade patterns (HGP) and predominant subtypes (PS). Following the system, this study aimed to explore the feasibility of predicting HGP and PS for IASLC grading.</p><p><strong>Materials and methods: </strong>A total of 529 ADCs from patients who underwent radical surgical resection were randomly divided into training and validation datasets in a 7:3 ratio. A two-step model consisting of two submodels was developed for IASLC grading. One submodel assessed whether the HGP exceeded 20% for ADCs, whereas the other distinguished between lepidic and acinar/papillary PS. The predictions from both submodels determined the final IASLC grades. Two variants of this model using either radiomic or clinical-semantic features were created. Additionally, one-step models that directly assessed IASLC grades using clinical-semantic or radiomic features were developed for comparison. The area under the curve (AUC) was used for model evaluation.</p><p><strong>Results: </strong>The two-step radiomic model achieved the highest AUC values of 0.95, 0.85, 0.96 for grades 1, 2, 3 among models. The two-step models outperformed the one-step models in predicting grades 2 and 3, with AUCs of 0.89 and 0.96 vs. 0.53 and 0.81 for radiomics, and 0.68 and 0.77 vs. 0.44 and 0.63 for clinical-semantics (p < 0.001). Radiomics models showed better AUCs than clinical-semantic models for grade 3 regardless of model steps.</p><p><strong>Conclusions: </strong>Predicting HGP and PS using radiomics can achieve accurate IASLC grading in ADCs. Such a two-step radiomics model may provide precise preoperative diagnosis, thereby supporting treatment planning.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"42"},"PeriodicalIF":3.5,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11951669/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143742530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of intrahepatic cholangiocarcinoma with LI-RADS in the high-risk population: MRI diagnosis and postoperative survival.","authors":"Ruofan Sheng, Beixuan Zheng, Yunfei Zhang, Chun Yang, Dong Wu, Jianjun Zhou, Mengsu Zeng","doi":"10.1186/s40644-025-00860-6","DOIUrl":"10.1186/s40644-025-00860-6","url":null,"abstract":"<p><strong>Background: </strong>The precise impact of LI-RADS-defined risk factors on the diagnosis and prognosis of intrahepatic cholangiocarcinoma (iCCA) remains unclear.</p><p><strong>Objective: </strong>To assess the value of LI-RADS categories and features for iCCA diagnosis, focusing on the diagnostic and prognostic implications of LI-RADS-defined risk factors.</p><p><strong>Methods: </strong>Totally 214 high risk patients, including 107 surgically-confirmed solitary iCCAs and 107 hepatocellular carcinomas (HCC) from two centers were retrospectively enrolled. Clinical and MRI features based on LI-RADS v2018 were compared, and the performance of targetoid features for discriminating iCCA was evaluated. Recurrence-free survival (RFS) was compared across different pathologic diagnoses and LI-RADS categories. Multivariate Cox analysis was performed to identify the independent risk factors for RFS.</p><p><strong>Results: </strong>In the LI-RADS defined high-risk patients, iCCAs differed from HCCs in MRI manifestation. The LR-M category enabled the accurate classification of most iCCAs (89/107, 83.2%), achieving high sensitivity (83.2%), specificity (85.1%), and accuracy (84.1%). The optimal diagnostic performance for iCCA was achieved when at least one targetoid appearance was required for LR-M categorization (AUC = 0.828). Although 26.2% iCCAs presented at least one major feature and 15.0% iCCAs were miscategorized as probably or definitely HCC, only one iCCA case was categorized as LR-5. RFS varied according to both pathologic diagnosis (P = 0.030) and LI-RADS category (P = 0.028), with LI-RADS category demonstrating an independent association with RFS (HR = 1.736, P = 0.033).</p><p><strong>Conclusions: </strong>In high-risk patients, iCCAs frequently exhibit HCC major features, leading to miscategorization as probable HCC. However, the LR-5 category remains highly specific for ruling out iCCA. Furthermore, in high-risk patients with solitary resected iCCA or HCC, LI-RADS category enables the prediction of postsurgical prognosis independently from pathological diagnosis.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"40"},"PeriodicalIF":3.5,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11938583/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143718184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The value of ¹⁸F-FDG PET/CT and ¹⁸F-DOPA PET/CT in determining the initial surgical strategy of patients with medullary thyroid cancer : Preoperative PET/CT imaging for medullary thyroid cancer.","authors":"Eline C Jager, Adrienne H Brouwers, Madelon J H Metman, Dilay Aykan, Lisa H de Vries, Lutske Lodewijk, Menno R Vriens, Schelto Kruijff, Thera P Links","doi":"10.1186/s40644-025-00862-4","DOIUrl":"10.1186/s40644-025-00862-4","url":null,"abstract":"<p><strong>Background: </strong>While total thyroidectomy with central neck dissection (CND) is standard for medullary thyroid cancer (MTC), performing a lateral neck dissection (LND) depends on locoregional metastatic spread and is usually decided per individual. This study evaluated the utility of preoperative PET/CT in staging patients at diagnosis and guiding the initial surgical plan, while also exploring the value of neck ultrasound, MRI, and CT.</p><p><strong>Methods: </strong>All MTC patients from two tertiary hospitals (2000 - 2020) were identified from two retrospective databases. All reports of neck ultrasounds, MRIs, CTs and PET/CTs < 8 months prior to primary surgery or < 4 months after MTC diagnosis were reviewed. The sensitivity and specificity of each imaging modality for locating locoregional lymph node metastases (LNM) was determined.</p><p><strong>Results: </strong>A total of 175 MTC patients were included (91 females and 57 hereditary MTCs). Median age at presentation was 52 years (IQR 38 - 62). Initial treatment included a total thyroidectomy, CND and LND in 155 (89%), 140 (80%) and 59 (33%) patients. Preoperative imaging of the neck included ultrasound (91, 52%), MRI (33, 19%) and CT (31, 18%). PET/CT imaging was performed in 56 (32%) patients (35 ¹⁸F-FDG PET/CTs and 33 ¹⁸F-DOPA PET/CTs). Sensitivity for LNM in the central compartment was 72%, 39%, 6%, 42% and 93% for ¹⁸F-FDG PET/CT, ¹⁸F-DOPA PET/CT, ultrasound, MRI and CT, respectively. Respective specificity rates were 80%, 100%, 100%, 71% and 100%. Sensitivity rates for lateral neck LNM were 89%, 81%, 77%, 76% and 75%, for ¹⁸F-FDG PET/CT, ¹⁸F-DOPA PET/CT, ultrasound, MRI and CT, while specificity rates were 100%, 100%, 75%, 78% and 50%, respectively. Twenty-three patients had distant metastases on imaging. In total, 14 ¹⁸F-FDG PET/CTs and 9 ¹⁸F-DOPA PET/CTs were made in these 23 patients (both in six patients). All but one PET/CT showed distant metastases.</p><p><strong>Conclusions: </strong>PET/CT is a powerful tool to detect locoregional LNM and can particularly help identify cases where LNDs are required, avoiding reoperation later on. For accurate staging of the central neck, PET may be combined with diagnostic CT. Finally, PET/CT's ability to detect distant metastases may support de-escalation of a surgical intervention when cure is unlikely.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"41"},"PeriodicalIF":3.5,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11938770/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Establishment of a deep-learning-assisted recurrent nasopharyngeal carcinoma detecting simultaneous tactic (DARNDEST) with high cost-effectiveness based on magnetic resonance images: a multicenter study in an endemic area.","authors":"Yishu Deng, Yingying Huang, Haijun Wu, Dongxia He, Wenze Qiu, Bingzhong Jing, Xing Lv, Weixiong Xia, Bin Li, Ying Sun, Chaofeng Li, Chuanmiao Xie, Liangru Ke","doi":"10.1186/s40644-025-00853-5","DOIUrl":"10.1186/s40644-025-00853-5","url":null,"abstract":"<p><strong>Background: </strong>To investigate the feasibility of detecting local recurrent nasopharyngeal carcinoma (rNPC) using unenhanced magnetic resonance images (MRI) and optimize a layered management strategy for follow-up with a deep learning model.</p><p><strong>Methods: </strong>Deep learning models based on 3D DenseNet or ResNet frames using unique sequence (T1WI, T2WI, or T1WIC) or a combination of T1WI and T2WI sequences (T1_T2) were developed to detect local rNPC. A deep-learning-assisted recurrent NPC detecting simultaneous tactic (DARNDEST) utilized DenseNet was optimized by superimposing the T1WIC model over the T1_T2 model in a specific population. Diagnostic efficacy (accuracy, sensitivity, specificity) and examination cost of a single MR scan were compared among the conventional method, T1_T2 model, and DARNDEST using McNemar's Z test.</p><p><strong>Results: </strong>No significant differences in overall accuracy, sensitivity, and specificity were found between the T1WIC model and T1WI, T2WI, or T1_T2 models in both test sets (all P > 0.0167). The DARNDEST had higher accuracy and sensitivity but lower specificity than the T1_T2 model in both the internal (accuracy, 85.91% vs. 84.99%; sensitivity, 90.36% vs. 84.26%; specificity, 82.20% vs. 85.59%) and external (accuracy, 86.14% vs. 84.16%; sensitivity, 90.32% vs. 84.95%; specificity, 82.57% vs. 83.49%) test sets. The cost of a single MR examination using DARNDEST was $330,724 (internal) and $328,971 (external) with a hypothetical cohort of 1,000 patients, relative to $313,250 of the T1_T2 model and $340,865 of the conventional method.</p><p><strong>Conclusions: </strong>Detecting local rNPC using unenhanced MRI with deep learning is feasible and DARNDEST-driven follow-up management is efficient and economic.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"39"},"PeriodicalIF":3.5,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11931764/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143699496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acinar cell carcinoma of the pancreas: can CT and MR features predict survival?","authors":"Monica Cheng, Nikita Consul, Ryan Chung, Carlos Fernandez- Del Castillo, Yasmin Hernandez-Barco, Avinash Kambadakone","doi":"10.1186/s40644-025-00859-z","DOIUrl":"10.1186/s40644-025-00859-z","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate the CT and MRI features of pancreatic acinar cell carcinoma (pACC) and their association with patient outcome and survival.</p><p><strong>Methods: </strong>This retrospective single-center study included 49 patients with pathology-proven pancreatic acinar cell carcinoma who underwent diagnostic imaging between August 1998 - September 2019. Two radiologists reviewed CT and MRI features independently. Survival was estimated using the Kaplan-Meier method, and Cox proportional-hazards regression model was used to identify factors associated with survival.</p><p><strong>Results: </strong>pACC tended to present as a solid (31/49, 63.3%) pancreatic head mass (26/49, 53.1%) with ill-defined margins (32/49, 65.3%) and median maximal diameter of 4.1 cm (IQR, 2.9-6.2). Majority of lesions were hypo- or isodense (38/49, 77.6%) compared to normal pancreatic parenchyma, with heterogenous (39/49, 79.6%) enhancement pattern. Biliary ductal dilatation was uncommon, with pancreatic ductal dilatation in 22.4% (11/49) and common bile duct dilatation in 14.3% (7/49). Intralesional calcifications were seen in 6.1% (3/49). Metastasis was present in 71.4% (35/49) of patients at the time of diagnosis. On MRI, 88.9% (16/18) demonstrated diffusion restriction and 59.1% (13/22) with heterogenous enhancement. On multivariate analysis, the imaging presence of T1 hyperintensity (p = 0.02), hypoattenuating necrotic components (p = 0.02), and splenic vein invasion (p = 0.04) were associated with worse survival.</p><p><strong>Conclusion: </strong>Pancreatic acinar cell carcinoma is a rare pancreatic neoplasm that often presents as a large ill-defined heterogeneously enhancing mass without biliary ductal dilation. T1 hyperintensity, presence of hypoattenuating necrotic components, and splenic vein invasion were independent predictors of survival.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"38"},"PeriodicalIF":3.5,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11929164/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143676920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LI-RADS-based hepatocellular carcinoma risk mapping using contrast-enhanced MRI and self-configuring deep learning.","authors":"Róbert Stollmayer, Selda Güven, Christian Marcel Heidt, Kai Schlamp, Pál Novák Kaposi, Oyunbileg von Stackelberg, Hans-Ulrich Kauczor, Miriam Klauss, Philipp Mayer","doi":"10.1186/s40644-025-00844-6","DOIUrl":"10.1186/s40644-025-00844-6","url":null,"abstract":"<p><strong>Background: </strong>Hepatocellular carcinoma (HCC) is often diagnosed using gadoxetate disodium-enhanced magnetic resonance imaging (EOB-MRI). Standardized reporting according to the Liver Imaging Reporting and Data System (LI-RADS) can improve Gd-MRI interpretation but is rather complex and time-consuming. These limitations could potentially be alleviated using recent deep learning-based segmentation and classification methods such as nnU-Net. The study aims to create and evaluate an automatic segmentation model for HCC risk assessment, according to LI-RADS v2018 using nnU-Net.</p><p><strong>Methods: </strong>For this single-center retrospective study, 602 patients at risk for HCC were included, who had dynamic EOB-MRI examinations between 05/2005 and 09/2022, containing ≥ LR-3 lesion(s). Manual lesion segmentations in semantic segmentation masks as LR-3, LR-4, LR-5 or LR-M served as ground truth. A set of U-Net models with 14 input channels was trained using the nnU-Net framework for automatic segmentation. Lesion detection, LI-RADS classification, and instance segmentation metrics were calculated by post-processing the semantic segmentation outputs of the final model ensemble. For the external evaluation, a modified version of the LiverHccSeg dataset was used.</p><p><strong>Results: </strong>The final training/internal test/external test cohorts included 383/219/16 patients. In the three cohorts, LI-RADS lesions (≥ LR-3 and LR-M) ≥ 10 mm were detected with sensitivities of 0.41-0.85/0.40-0.90/0.83 (LR-5: 0.85/0.90/0.83) and positive predictive values of 0.70-0.94/0.67-0.88/0.90 (LR-5: 0.94/0.88/0.90). F1 scores for LI-RADS classification of detected lesions ranged between 0.48-0.69/0.47-0.74/0.84 (LR-5: 0.69/0.74/0.84). Median per lesion Sørensen-Dice coefficients were between 0.61-0.74/0.52-0.77/0.84 (LR-5: 0.74/0.77/0.84).</p><p><strong>Conclusion: </strong>Deep learning-based HCC risk assessment according to LI-RADS can be implemented as automatically generated tumor risk maps using out-of-the-box image segmentation tools with high detection performance for LR-5 lesions. Before translation into clinical practice, further improvements in automatic LI-RADS classification, for example through large multi-center studies, would be desirable.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"36"},"PeriodicalIF":3.5,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11912691/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143646901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}